This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

Ⅰ. INTRODUCTION
Ⅱ. MATERIALS AND METHODS
    1. Data collection
    2. Processing climate data
    3. Yield prediction model construction
    4. Calculating damage by abnormal climate
    5. Mapping as damage of abnormal climate
Ⅲ. RESULTS AND DISCUSSION
    1. Calculated dry matter yield of IRG by abnormalclimate level
    2. Calculated damage of IRG by abnormal climatelevel
    3. Mapping of the damage by abnormal climate level
Ⅳ. ACKNOWLEDGEMENTS

• Ji Yung Kim(College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)
• Moonju Kim(Institute of Animal Resources, Kangwon National University, Chuncheon 24341, Republic of Korea)
• Kyung Il Sung(College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)
• Byong Wan Kim(College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea) Corresponding author
• Jae Seong Choi(College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)